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Quality-aware skill translation models for expert finding on stackoverflow

INFORMATION SYSTEMS 2020年 87卷 101413-000页

作者： Nobari, Arash Dargahi Neshati, Mahmood Gharebagh, Sajad Sotudeh Shahid Beheshti Univ Fac Comp Sci & Engn Gc Iran

stackoverflow has become an emerging resource for talent recognition in recent years. While users exploit technical language on stackoverflow, recruiters try to find the relevant candidates for jobs using their own terminology. This procedure implies a gap which exists between recruiters and candidates terms. Due to this gap, the state-of-the-art expert finding models cannot effectively address the expert finding problem on stackoverflow. We propose two translation models to bridge this gap. The first approach is a statistical method and the second is based on word embedding approach. Utilizing several translations for a given query during the scoring step, the result of each intermediate query is blended together to obtain the final ranking. Here, we propose a new approach which takes the quality of documents into account in scoring step. We have made several observations to visualize the effectiveness of the translation approaches and also the quality-aware scoring approach. Our experiments indicate the following: First, while statistical and word embedding translation approaches provide different translations for each query, both can considerably improve the recall. Besides, the quality-aware scoring approach can improve the precision remarkably. Finally, our best proposed method can improve the MAP measure up to 46% on average, in comparison with the state-of-the-art expert finding approach. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Expertise retrieval Statistical machine translation Semantic matching stackoverflow Expert finding Word embedding

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On code reuse from stackoverflow: An exploratory study on Android apps

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INFORMATION AND SOFTWARE TECHNOLOGY 2017年 88卷 148-158页

作者： Abdalkareem, Rabe Shihab, Emad Rilling, Juergen Concordia Univ Data Driven Anal Software DAS Lab Montreal PQ Canada Concordia Univ Dept Comp Sci & Software Engn Montreal PQ Canada

Context: Source code reuse has been widely accepted as a fundamental activity in software development. Recent studies showed that stackoverflow has emerged as one of the most popular resources for code reuse. Therefore, a plethora of work proposed ways to optimally ask questions, search for answers and find relevant code on stackoverflow. However, little work studies the impact of code reuse from Stack Overflow. Objective: To better understand the impact of code reuse from stackoverflow, we perform an exploratory study focusing on code reuse from stackoverflow in the context of mobile apps. Specifically, we investigate how much, why, when, and who reuses code. Moreover, to understand the potential implications of code reuse, we examine the percentage of bugs in files that reuse stackoverflow code. Method: We perform our study on 22 open source Android apps. For each project, we mine their source code and use clone detection techniques to identify code that is reused from stackoverflow. We then apply different quantitative and qualitative methods to answer our research questions. Results: Our findings indicate that 1) the amount of reused stackoverflow code varies for different mobile apps, 2) feature additions and enhancements in apps are the main reasons for code reuse from stackoverflow, 3) mid-age and older apps reuse stackoverflow code mostly later on in their project lifetime and 4) that in smaller teams/apps, more experienced developers reuse code, whereas in larger teams/apps, the less experienced developers reuse code the most. Additionally, we found that the percentage of bugs is higher in files after reusing code from stackoverflow. Conclusion: Our results provide insights on the potential impact of code reuse from stackoverflow on mobile apps. Furthermore, these results can benefit the research community in developing new techniques and tools to facilitate and improve code reuse from stackoverflow. (C) 2017 Elsevier B.V. All rights reserved.

关键词： stackoverflow Mobile app Code reuse

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Automatic mapping of user tags to Wikipedia concepts: The case of a Q&A website - stackoverflow

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JOURNAL OF INFORMATION SCIENCE 2015年第5期41卷 570-583页

作者： Joorabchi, Arash English, Michael Mahdi, Abdulhussain E. Univ Limerick Dept Elect & Comp Engn Limerick Ireland Univ Limerick Dept Comp Sci & Informat Syst Limerick Ireland

The uncontrolled nature of user-assigned tags makes them prone to various inconsistencies caused by spelling variations, synonyms, acronyms and hyponyms. These inconsistencies in turn lead to some of the common problems associated with the use of folksonomies such as the tag explosion phenomenon. Mapping user tags to their corresponding Wikipedia articles, as well-formed concepts, offers multifaceted benefits to the process of subject metadata generation and management in a wide range of online environments. These include normalization of inconsistencies, elimination of personal tags and improvement of the interchangeability of existing subject metadata. In this article, we propose a machine learning-based method capable of automatic mapping of user tags to their equivalent Wikipedia concepts. We have demonstrated the application of the proposed method and evaluated its performance using the currently most popular computer programming Q&A website, ***, as our test platform. Currently, around 20 million posts in stackoverflow are annotated with about 37,000 unique user tags, from which we have chosen a subset of 1256 tags to evaluate the accuracy performance of our proposed mapping method. We have evaluated the performance of our method using the standard information retrieval measures of precision, recall and F-1. Depending on the machine learning-based classification algorithm used as part of the mapping process, F-1 scores as high as 99.6% were achieved.

关键词： Semantic mapping stackoverflow subject metadata user tags Wikipedia

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Architectural Knowledge for Technology Decisions in Developer Communities An Exploratory Study with stackoverflow 13

Architectural Knowledge for Technology Decisions in Develope...

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13th Working IEEE/IFIP Conference on Software Architecture (WICSA)

作者： Soliman, Mohamed Galster, Matthias Salama, Amr R. Riebisch, Matthias Univ Hamburg Dept Informat Hamburg Germany Univ Canterbury Christchurch New Zealand

ISBN: (纸本)9781509021314

Architectural decisions have a big influence on basic properties of a software system, and are difficult to change once implemented. Technology decisions deserve special attention because they are one of the most frequently occurring types of architectural decisions. Architectural knowledge is crucial for good decisions. Current architecture knowledge management approaches try to support architects by offering a rich base of architectural solutions and design decision rules. However, they mostly depend on manually capturing and maintaining the architectural knowledge. In this paper, we utilize the most popular online software development community (stackoverflow) as a source of knowledge for technology decisions to support architecture knowledge management approaches with a more efficient methods for knowledge capturing. We conducted an exploratory study, and followed a qualitative and quantitative content analysis approach. We analysed the posts in this community to identify architecture-relevant and technology-related knowledge, and to classify the posts into different types for the purpose of knowledge structuring. In addition, we evaluated our findings through feedback from practitioners.

关键词： architecture design decisions Architecture knowledge empirical study knowledge capturing knowledge management software architecture stackoverflow

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Reusing Code from stackoverflow: The Effect on Technical Debt 45

Reusing Code from StackOverflow: The Effect on Technical Deb...

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45th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) / 22nd Euromicro Conference on Digital System Design (DSD)

作者： Nikolaidis, Nikolaos Digkas, Georgios Ampatzoglou, Apostolos Chatzigeorgiou, Alexander Univ Macedonia Dept Appl Informat Thessaloniki Greece

ISBN: (纸本)9781728134215

Software reuse is a well-established software engineering process that aims at improving development productivity. Although reuse can be performed in a systematic way (e.g., through product lines), in practice, reuse is performed in many cases opportunistically, i.e., copying small code chunks either from the web or in-house developed projects. Knowledge sharing communities and especially stackoverflow constitute the primary source of code-related information for amateur and professional software developers. Despite the obvious benefit of increased productivity, reuse can have a mixed effect on the quality of the resulting code depending on the properties of the reused solutions. An efficient concept for capturing a wide-range of internal software qualities is the metaphor of Technical Debt which expresses the impact of shortcuts in software development on its maintenance costs. In this paper, we present the results of an empirical study on the relation between the existence of reusing code retrieved from stackoverflow on the technical debt of the target system. In particular, we study several open-source projects and identify non-trivial pieces of code that exhibit a perfect or near-perfect match with code provided in the context of answers in stackoverflow. Then, we compare the technical debt density of the reused fragments, obtained as the ratio of inefficiencies identified by SonarQube over the lines of reused code, to the technical debt density of the target codebase. The results provide insights to the potential impact of small-scale code reuse on technical debt and highlight the benefits of assessing code quality before committing changes to a repository.

关键词： Technical Debt stackoverflow Code Reuse Software Quality

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Assessing Text Representation Methods on Tag Prediction Task for stackoverflow 56

Assessing Text Representation Methods on Tag Prediction Task...

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56th Annual Hawaii International Conference on System Sciences (HICSS)

作者： Skenderi, Erjon Laaksonen, Salla-Maaria Huhtamaki, Jukka Stefanidis, Kostas Tampere Univ Tampere Finland Univ Helsinki Helsinki Finland

ISBN: (纸本)9780998133164

A large part of knowledge evolves outside of the operations of an organization. Question and answer online social platforms provide an important source of information to explore the underlying communities. stackoverflow (SO) is one of the most popular question and answer platforms for developers, with more than 23 million questions asked. Organizing and categorizing data is crucial to manage knowledge in such large quantities. Questions posted on SO are assigned a set of tags and textual content of each question may contain coding syntax. In this paper, we evaluate the performance of multiple text representation methods in the task of predicting tags for SO questions and empirically prove the impact of code syntax in text representations. The SO dataset was sampled and questions without code syntax were identified. Two classical text representation methods consisting of BoW and TF-IDF were selected along four other methods based on pre-trained models including Fasttext, USE, Sentence-BERT and Sentence-RoBERTa. Multi-label k'th Nearest Neighbors classifier was used to learn and predict tags based on the similarities between feature-vector representations of the input data. Our results indicate a consistent superiority of the representations generated from Sentence-RoBERTa. Overall, the classifier achieved a 17% or higher improvement on F1 score when predicting tags for questions without any code syntax in content.

关键词： Text representation Tag prediction Q&A forums stackoverflow Knowledge-intensive work

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Searching stackoverflow Questions with Multi-Faceted Categorization 18

Searching StackOverflow Questions with Multi-Faceted Categor...

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10th Asia-Pacific Symposium on Internetware (Internetware)

作者： Liu, Mingwei Peng, Xin Jiang, Qingtao Marcus, Andrian Yang, Junwen Zhao, Wenyun Fudan Univ Sch Comp Sci Shanghai Peoples R China Univ Texas Dallas Dept Comp Sci Richardson TX 75083 USA Fudan Univ Shanghai Key Lab Data Sci Shanghai Peoples R China

ISBN: (纸本)9781450365901

stackoverflow provides answers for a huge number of software development questions that are frequently encountered by developers. However, searching relevant questions in stackoverflow is not always easy using the keyword based search engine provided by stackoverflow. A software development question can be characterized by multiple attributes, such as, its concern (e.g., configuration problem, error handling, sample code, etc.), programming language, operating system, and involved middleware, framework, library and software technology. We propose a multi-faceted and interactive approach for searching stackoverflow questions (called MFISSO), which leverages these attributes of the questions. Our approach starts with an initial keyword-based query and extracts a multifaceted categorization from all the candidate questions using natural language processing and data mining. It then allows developers to iteratively refine the search results through an interactive process. We evaluated an implementation of MFISSO in a controlled experiments with 20 computing students, solving ten software development tasks using stackoverflow. The experiment shows that MFISSO can help developers find relevant questions faster and with higher accuracy.

关键词： Information Retrieval stackoverflow Multi-faceted Categorization

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Investigating Web3D topics on stackoverflow: a preliminary study of WebGL and *** 20

Investigating Web3D topics on StackOverflow: a preliminary s...

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25th ACM Conference on 3D Web Technology (Web3D)

作者： Almansoury, Farag Kpodjedo, Segla El Boussaidi, Ghizlane ETS Montreal Montreal PQ Canada

ISBN: (纸本)9781450381697

Web3D developers often have to decide on which technologies are best for their projects. We explore that question through the perspective of community attention and support on Stack Overflow (SO). We focused on i) WebGL, a key low-level JavaScript ( JS) API used to render 3D graphics in browsers without plugins, and ii) ***, a higher level JS library that reuses WebGL and is reputed easier and more intuitive. We considered questions from SO tagged with WebGL or *** and extracted all tags used on these questions. Using these, we were able to compare the relative attention (considering the number of questions and views) and support (considering satisfactory answers and how long they take) received by concerns and technologies associated to WebGL and ***. Our results suggest that *** gets significantly more community attention but less community support than WebGL on SO.

关键词： Web3D WebGL Three.js stackoverflow empirical study

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A Journey of Bounty Hunters: Analyzing the Influence of Reward Systems on stackoverflow Question Response Times

A Journey of Bounty Hunters: Analyzing the Influence of Rewa...

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IEEE/WIC/ACM International Conference on Web Intelligence (WI)

作者： Berger, Philipp Hennig, Patrick Bocklisch, Tom Herold, Tom Meinel, Christoph Univ Potsdam Hasso Plattner Inst Potsdam Germany

ISBN: (纸本)9781509044702

Question and Answering (Q&A) platforms are an important source for information and a first place to go when searching for help. Q&A sites, like stackoverflow (SO), use reward systems to incentivize users to answer fast and accurately. In this paper we study and predict the response time for those questions on stackoverflow, that benefit from an additional incentive through so called bounties. Shaped by different motivations and rules these questions perform unlike regular questions. As our key finding we note that topic related factors provide a much stronger evidence than previously found factors for these questions. Finally, we compare models based on these features predicting the response time in the context of bounty questions.

关键词： Bounty Prediction Question answering Response times stackoverflow

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Reinforcing Diversity Company Policies: Insights from stackoverflow Developers Survey 21

Reinforcing Diversity Company Policies: Insights from StackO...

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21st International Conference on Enterprise Information Systems (ICEIS)

作者： Silveira, Karina Kohl Musse, Soraia Manssour, Isabel Vieira, Renata Prikladnicki, Rafael Pontificia Univ Catolica Rio Grande do Sul PUCRS Sch Technol Porto Alegre RS Brazil

ISBN: (纸本)9789897583728

Diversity is being intensively discussed by different knowledge areas of society and discussions in Software Engineering, are increasing as well. There are unconscious bias and lack of representativeness and when we talk about characteristics as ethnicity and gender, to mention a few. How can tech companies support diversity, minimizing unconscious bias in their teams? Studies say that diversity builds better teams and delivers better results, among other benefits. Cognitive diversity is linked to better outcomes, and is influenced by identity diversity (e.g., gender, race, etc.), mainly when tasks are related to problem-solving and prediction. In this work, we are interested in understanding the pain points in software engineering regarding diversity and provide insights to support the attraction, hiring and retention policies for more diverse software engineering environments. stackoverflow is a popular community question&answer forum, with a high engagement of software developers. Yearly, they apply a survey, present straightforward results, and made the anonymized results available for download. So, it is possible to perform additional analysis beyond the original ones. Using data visualization techniques, we analyzed 2018 data implying insights and recommendations. Results show that diversity in companies is not yet a conscious decision-making factor for developers assessing a new job opportunity, and respondents from underrepresented groups tend to believe more they are not as good as their peers. We also propose a discussion about the unconscious bias, stereotypes, and impostor syndrome and how to provide support on that.

关键词： Software Engineering Software Development Diversity Data Visualization stackoverflow

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